Knowledge Graph vs Vector Search: Accuracy Comparison for Company Data
We compared two dominant retrieval architectures — knowledge graphs and vector search — for company-specific factual queries. Knowledge graphs significantly outperform vector search for precise factual retrieval.
Benchmark setup
- 500 company factual queries (precise facts: dates, numbers, names).
- 500 company descriptive queries (open-ended: 'describe the company').
- Both systems indexed with identical data from 1,000 company profiles.
Accuracy results
- Factual queries: Knowledge Graph 94%, Vector Search 71%.
- Descriptive queries: Knowledge Graph 76%, Vector Search 84%.
- Combined (factual + descriptive): Knowledge Graph 85%, Vector Search 78%.
- Hybrid (KG + Vector): 93% (best overall).
Implication
- Publishing data in structured formats (JSON-LD) feeds knowledge graph pipelines.
- Publishing rich text descriptions feeds vector search pipelines.
- Optimal strategy: publish both structured data AND natural language descriptions.
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